Physiological and metabolomic analyses reveal that FeO nanoparticles ameliorate cadmium and arsenic toxicity in Panax notoginseng.

Heavy metal(loid)-contaminated available arable land seriously affects crop development and growth. Engineered nanomaterials have great potential in mitigating toxic metal(loid) stress in plants. However, there are few details of nanoparticles (NPs) involved in Panax notoginseng response to cadmium (Cd) and arsenic (As).

Effect of Nitrogen Addition on Selection of Germination Trait in an Alpine Meadow on the Tibet Plateau.

Seed germination requirements may determine the kinds of habitat in which plants can survive. We tested the hypothesis that nitrogen (N) addition can change seed germination trait-environmental filter interactions and ultimately redistribute seed germination traits in alpine meadows. We determined the role of N addition on germination trait selection in an alpine meadow after N addition by combining a 3-year N addition experiment in an alpine meadow and laboratory germination experiments.

Impacts of growth form and phylogenetic relatedness on seed germination: A large-scale analysis of a subtropical regional flora.

Plant regeneration strategy plays a critical role in species survival and can be used as a proxy for the evolutionary response of species to climate change. However, information on the effects of key plant traits and phylogenetic relatedness on seed germination is limited at large regional scales that vary in climate. To test the hypotheses that phylogenetic niche conservatism plays a critical force in shaping seed ecophysiological traits across species, and also drives their response to climatic fluctuation, we conducted a controlled experiment on seed germination and determined the percentage and rate of germination for 249 species in subtropical China under two temperature regimes (i.

A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration.

Synthetic Aperture Radar (SAR) scene classification is challenging but widely applied, in which deep learning can play a pivotal role because of its hierarchical feature learning ability. In the paper, we propose a new scene classification framework, named Feature Recalibration Network with Multi-scale Spatial Features (FRN-MSF), to achieve high accuracy in SAR-based scene classification. First, a Multi-Scale Omnidirectional Gaussian Derivative Filter (MSOGDF) is constructed.